An Integrated Framework for Web Data Preprocessing Towards Modeling User Behavior

Ali, Noaman M.; Gadallah, Ahmed M.; Hefny, Hesham A.; Novikov, Boris

doi:10.1109/fareastcon50210.2020.9271467

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Generally, the proposed model incorporates several subtasks starting from collecting the customers' reviews, followed by a series of data processing tasks, ending with providing a list of prominent aspects of the target entity, as shown in Figure 2. Data collection and preprocessing is the first step toward achieving the desired task [47,48]. Clustering features accept only numerical data; therefore, getting a numerical representation of textual data is imperative.…”

Section: The Framework Descriptionmentioning

confidence: 99%

Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

et al. 2022

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Sentiment analysis on social media and e-markets has become an emerging trend. Extracting aspect terms for structure-free text is the primary task incorporated in the aspect-based sentiment analysis. This significance relies on the dependency of other tasks on the results it provides, which directly influences the accuracy of the final results of the sentiment analysis. In this work, we propose an aspect term extraction model to identify the prominent aspects. The model is based on clustering the word vectors generated using the pre-trained word embedding model. Dimensionality reduction was employed to improve the quality of word clusters obtained using the K-Means++ clustering algorithm. The proposed model was tested on the real datasets collected from online retailers’ websites and the SemEval-14 dataset. Results show that our model outperforms the baseline models.

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Section: The Framework Descriptionmentioning

confidence: 99%

Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

et al. 2022

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“…The phase incorporates the following steps: Data Cleaning. Cleaning and filtering of irrelevant, redundant, and noisy data that is not suitable for the mining process [32,33].…”

Section: Data Processingmentioning

confidence: 99%

Online web navigation assistant

Ali

Gadallah

Hefny

et al. 2021

Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki

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The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

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“…Social scientists and program implementers are aware of the effects of behavioral heterogeneity and have used psychographic-behavioral segmentation to group study subjects based on their preferences, beliefs, and self-reported behaviors [ 11 ]. We believe that online behavior, which has fueled extensive behavioral modeling in computer science [ 12 , 13 ], provides another useful dimension for behavioral segmentation in global health. Segmenting users based on online behavior may allow us to better understand, predict, and support the uptake of EBCRs among different populations.…”

Section: Introductionmentioning

confidence: 99%

Segmenting Clinicians’ Usage Patterns of a Digital Health Tool in Resource-Limited Settings: Clickstream Data Analysis and Survey Study

Miller¹,

Rosenberg²,

Pickard³

et al. 2022

JMIR Form Res

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Background Evidence-based digital health tools allow clinicians to keep up with the expanding medical literature and provide safer and more accurate care. Understanding users’ online behavior in low-resource settings can inform programs that encourage the use of such tools. Our program collaborates with digital tool providers, including UpToDate, to facilitate free subscriptions for clinicians serving in low-resource settings globally. Objective We aimed to define segments of clinicians based on their usage patterns of UpToDate, describe the demographics of those segments, and relate the segments to self-reported professional climate measures. Methods We collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percentage of days active online, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics and related them to survey data. Results We enrolled 1681 clinicians from 75 countries over a 9-week period. We based the following five behavioral segments on the length and intensity of use: short-term, light users (420/1681, 25%); short-term, heavy users (252/1681, 15%); long-term, heavy users (403/1681, 24%); long-term, light users (370/1681, 22%); and never-users (252/1681, 15%). Users spent a median of 5 hours using the tool over the year. On days when users logged on, they spent a median of 4.4 minutes online and an average of 71% of their time reading medical content as opposed to navigating or managing their account. Over half (773/1432, 54%) of the users actively used the tool for 48 weeks or more during the 52-week study period. The distribution of segments varied by age, with lighter and less use among those aged 35 years or older compared to that among younger users. The speciality of medicine had the heaviest use, and emergency medicine had the lightest use. Segments varied strongly by geographic region. As for professional climate, most respondents (1429/1681, 85%) reported that clinicians in their area would view the use of a online tool positively, and compared to those who reported other views, these respondents were less likely to be never-users (286/1681, 17% vs 387/1681, 23%) and more likely to be long-term users (655/1681, 39% vs 370/1681, 22%). Conclusions We believe that these behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods for combining clickstream data with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives digital health tool use.

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An Integrated Framework for Web Data Preprocessing Towards Modeling User Behavior

Cited by 5 publications

References 23 publications

Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

Online web navigation assistant

Segmenting Clinicians’ Usage Patterns of a Digital Health Tool in Resource-Limited Settings: Clickstream Data Analysis and Survey Study

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